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A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators

Oliver Urs Lenz (UGent) , Daniel Peralta (UGent) and Chris Cornelis (UGent)
(2019)
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Abstract
Fuzzy rough sets have been successfully applied in classification tasks, in particular in combination with OWA operators. There has been a lot of research into adapting algorithms for use with Big Data through parallelisation, but no concrete strategy exists to design a Big Data fuzzy rough sets based classifier. Existing Big Data approaches use fuzzy rough sets for feature and prototype selection, and have often not involved very large datasets. We fill this gap by presenting the first Big Data extension of an algorithm that uses fuzzy rough sets directly to classify test instances, a distributed implementation of FRNN-OWA in Apache Spark. Through a series of systematic tests involving generated datasets, we demonstrate that it can achieve a speedup effectively equal to the number of computing cores used, meaning that it can scale to arbitrarily large datasets.
Keywords
fuzzy rough sets, OWA operators, Big Data, Apache Spark

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Citation

Please use this url to cite or link to this publication:

Chicago
Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. 2019. “A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators.” In .
APA
Lenz, O. U., Peralta, D., & Cornelis, C. (2019). A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators. Presented at the International Joint Conference on Rough Sets 2019.
Vancouver
1.
Lenz OU, Peralta D, Cornelis C. A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators. 2019.
MLA
Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. “A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators.” 2019. Print.
@inproceedings{8612581,
  abstract     = {Fuzzy rough sets have been successfully applied in classification tasks, in particular in combination with OWA operators. There has been a lot of research into adapting algorithms for use with Big Data through parallelisation, but no concrete strategy exists to design a Big Data fuzzy rough sets based classifier. Existing Big Data approaches use fuzzy rough sets for feature and prototype selection, and have often not involved very large datasets. We fill this gap by presenting the first  Big Data extension of an algorithm that uses fuzzy rough sets directly to classify test instances, a distributed implementation of FRNN-OWA in Apache Spark. Through a series of systematic tests involving generated datasets, we demonstrate that it can achieve a speedup effectively equal to the number of computing cores used, meaning that it can scale to arbitrarily large datasets.},
  author       = {Lenz, Oliver Urs and Peralta, Daniel and Cornelis, Chris},
  location     = {Debrecen},
  title        = {A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators},
  year         = {2019},
}